Identifying biomarkers from mass spectrometry data with ordinal outcome

Deukwoo Kwon, Mahlet G. Tadesse, Naijun Sha, Ruth M. Pfeiffer, Marina Vannucci

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

In recent years, there has been an increased interest in using protein mass spectroscopy to identify molecular markers that discriminate diseased from healthy individuals. Existing methods are tailored towards classifying observations into nominal categories. Sometimes, however, the outcome of interest may be measured on an ordered scale. Ignoring this natural ordering results in some loss of information. In this paper, we propose a Bayesian model for the analysis of mass spectrometry data with ordered outcome. The method provides a unified approach for identifying relevant markers and predicting class membership. This is accomplished by building a stochastic search variable selection method within an ordinal outcome model. We apply the methodology to mass spectrometry data on ovarian cancer cases and healthy individuals. We also utilize wavelet-based techniques to remove noise from the mass spectra prior to analysis. We identify protein markers associated with being healthy, having low grade ovarian cancer, or being a high grade case. For comparison, we repeated the analysis using conventional classification procedures and found improved predictive accuracy with our method.

Original languageEnglish (US)
Pages (from-to)19-28
Number of pages10
JournalCancer Informatics
Volume3
DOIs
StatePublished - 2007
Externally publishedYes

Keywords

  • Markov chain Monte Carlo
  • Mass spectrometry
  • Ordinal outcome
  • Variable selection

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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